An Analysis and Design of Frozen Shrimp Traceability System Based on Digital Business Ecosystem
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Faculty of Computer Science - Universitas Indonesia ©2014
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Faculty of Computer Science - Universitas Indonesia ©2014
ICACSIS 2014
International Conference on
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2014 International Conference on
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An Analysis and Design of Frozen Shrimp
Traceability System Based on
Digital Business Ecosystem
Taufik Djatna and Aditia Ginantaka
Graduate Program of Agro-industrial Technology, Bogor Agricultural University, Indonesia
taufikdjatna@ipb.ac.id, aditiaginantaka@gmail.com
Abstract—Traceability system is one of the most
critical requirements in logistic information
systems and the supply chain risk management for
both global food safety and quality assurance.
Real-time documentation from the earlier stages of
production process enabled the two way process of
traceability. This paper presented an analysis and
design for traceability system of frozen Vanname
shrimp based on digital business ecosystems (DBE)
model. Business Process Model Notation (BPMN
2.0) was the primary tool in analyzing the task for
capturing and transferring data processing
between traceable units in each layer of DBE.
Business process analysis helped to understand the
capturing steps as the main element within such
traceability system. The results of the analysis
showcased how traceability system work in digital
business ecosystem which involved on dispersed
stakeholders. Manual data transformation to the
digital system was provided by stakeholders using
digital species metaphors. The requirement for
factor analysis was computed with Relief method
to select the most important attribute to capture.
Our evaluation showed that the proposed system
was able for estimating water salinity and related
hatchery parameters changing, such as broodstock
ID which utilized as key code. Current results
showed the readiness of application to transfer into
real world operation.
Keywords-traceability, digital business ecosystem,
traceable unit, digital species
I.
INTRODUCTION
Traceability is the ability to verify the history,
location, or application of an item by means of
documented recorded identification. Other common
definitions
include
the
capability
(and
implementation) of keeping track of a given set or
type of information to a given degree, or the ability to
chronologically interrelate uniquely identifiable
entities in a way that is verifiable [1].
Traceability system could use to solve the food
safety problems. The food safety context related to the
efficacy of comestibles that can become the cause of
diseases (bacteria, viruses, and germs) from one
country to others will thus harm people’s health when
consumed. This condition risked the potency of
rejection from importer countries. Several regulations
that control food safety give requirement to the
producers of comestibles to create a traceability
system. This system is used to trace and track the flow
of products on every supply chain mechanism in the
production and its distribution processes.
The traceability system offers some benefits
particularly in products with fresh and easily damaged
characteristics such as vegetables, fruit, meat, milk
and fish [2]. One of the efficacies of this system is
that it can improve security towards food safety from
the products it produced. This system enables
elimination towards unqualified food products from
the supply chain system as well as monitoring the
environment condition that can influence the safety of
products. Product certification gives significant
influence in costumer confidence. Thus, traceability
system is one of the requirements in ISO standard.
ISO 22005 is one of the documents that regulated the
design and implementation of traceability system in a
feed and food supply chain [3].
Shrimp is one of the leading exports of fishery
commodities. Indonesian export volume data show
that 122 tons shrimp has been exported in 2012 [4].
The shrimp company has to meet the criteria in ISO
standard which is regulated the traceability system to
get the confidence of importers, therefore the rejection
can be avoided. The traceability system is established
by documenting exceptional information on every
point of supply chain and process stages of product
handling, thus food producers are able to give detail
information on the food products. The information of
the product collected by supply chain actor, then
shared to another stakeholders. This process just
happen when the actor become the member of
business ecosystem in shrimp production. Therefore,
it is required to model the process of documenting
information on every point of supply chain which
catalyzed by ICTs infrastructure. Several researchers
have been conducted to find out the implementation
of the traceability system, such as in vegetables
supply chain [1], on soya beans [5] and in supply
chain of aquaculture products [7].
The utilization of digital technology has formed a
digital ecosystem in its business activity and therefore
called as Digital Business Ecosystem (DBE). The data
of documentation results can be stored in a database
hence query can be conducted for the search process.
The development of traceability system on DBE
base replaces all process documentation by using
paper (paper based), thus resulting paperless
document which benefits in the improvement in
354
ICACSIS 2014
overcoming easily damaged products by recording the
tracks of product quality, the improvement in product
management recall, the automatic scanning, the
improvement in stock management, and the decrease
in both work force operational cost. The pilots
traceability system based on DBE have been
implemented in a private sector company which
located in some sparsed Island. Their main business
was on hatchery and processed food. Frozen Vanname
shrimp chosen as the model because the complete
process of the supply chain, start from breeding unit
until the retailer or restaurant. This system’s
constructions were in terms of interest a company
which located in some sparse location of the
Indonesian archipelago. Each actor in the traceability
system is a model of organism in Digital Business
Ecosystem which interact each other in data capture
and tracing process. Through the development of
Digital Business Ecosystem, every technical phase in
the process of documenting data can be conducted by
using the support of ICT based infrastructure that is
analogized as digital species in digital ecosystem. The
technique of product search can also be represented in
the form of formulation and logic of computer
programming hence the digital application design is
attained for the process of data documenting and
searching (Fig.1).
Fig 1.Structural Coupling between supply chain
ecosystem and digital ecosystem in traceability
system [6]
The objective of this work were to analyze the
requirement and to design of traceability system. We
then focus to the proposed system for frozen
Vanname shrimp products and then verify and
validate the traceability system to evaluate system
performance.
I.
REQUIREMENT ANALYSIS
A. Business Process Analysis
Business process in traceability system is modeled
in BPMN 2.0. The Development of BPMN is
conducted. It is started from the making of simple
flow chart, granting information related roles, process,
data and information to description, therefore it can be
analyzed and simulated [8]. System analysis is
conducted for parse a system be resolved into
components so it the interaction between components
and its environment can be seen. Results of analysis
showed the capacity of the system as seen from its
ability to add value from input to output [9]. Based on
the business process analysis then retrieved five
stakeholders who take a role in the system of
traceability and divided into four structure systems.
1. Input
This system requires data related to product,
processes and product quality as main input [10].
Data related to product include its product identity
code along with various identity components that
support the formation of these products.
Meanwhile, the data related to the process cover
some of the indicators of the process that are set
up on the stage of the seed production. Among
them are pH of water, water salinity, survival rate,
and temperature of water. The data related to the
quality of the standard value according to SNI are
the total plate count (TPC), the levels of lead, the
levels of histamine and others. This system,
however, is not documenting related data quality
due to lack of infrastructure system.
2. Pre-process
The results of identification of the data attribute
are then observed and documented in the
application form for a period of time during
material handling process.
3. Process
The main processes include documentation of
process traceability system using an application
data input of each stakeholder and tracing product
process from end user stakeholder. Every
stakeholder
performs
the
process
of
documentation into application data input that was
installed in the desktop computer on each unit of
stakeholder. The process is then continued by
printing the report in the form of label contains
barcode of seed ID and destination pool for the
process of enlargement shrimp larvae. The
barcodes on the labels function as product
identification that can be read using barcode
scanner. Readable barcode labels subsequently can
be added to the data on the next process. The
barcode is printed back and imprinted on the next
product label [11].
Quantitative model in the process of
documentation of the data was then factorized
using Relief method. This analysis was performed
to find out the most influential data attribute in
traceable unit. There were some attribute data
defined as variables which were analyzed by using
Relief algorithm such as survive rates, average
pond water temperature, pH of water and water
salinity. That variable used as quality process
parameter in seed production, thus the variable
have to documented for complete information.
Different probability of the attribute X data value
calculated as follow [12]:
355
W [X ] =
pequal × Gini ' ( X )
psamecl (1 − psamecl )
(1)
ICACSIS 2014
Where,
A. Capturing Data
P (V )2
2
2
Gini ' ( X ) = ∑
× ∑ P ( C | V ) − ∑ P (C )
2
C
x∈ X ∑ P (V )
C
V
(2)
W [X] is an approximation of the following difference
of probabilities of attribute X, P equal is P(equal value
of X), P samecl is P(same class), P(V) is probability of
value, and P(C) is probable classes, P(C|V) is
probability X value occurred in a certain class C.
As a system, traceability must fulfill basic architecture
of Input-Process-Output components. The design of
data flow which transforms inputs into outputs is
represented in Fig. 2.
Every stakeholder carried out documenting
process into data input application installed on
desktop computer in each stakeholder unit. As an
example, in the unit of seeds provision, the
documented data attribute among others were
broodstock ID, seeds ID, provided feed mill supplier,
water temperature of the seeds pond, pH and water
salinity. The entire data attribute was documented
during the activity of seeds handling process in the
data application form on breeding unit. Figure of
business process stage of seeds documenting data can
be seen on Fig 3.
System Environment
Stakeholder:
- Breeding unit
- Ongrowing unit
- Processing unit
etc
Input:
Data Product
Data Process
Role:
- Capture data
- Tracing process
Output:
Tracing report
Traceability
System
Regulation
Recall Report
Digital
Device
Fig 2.The design of the data flow that transforms
inputs and outputs
4. Output
The data set will store in the traceability repository
include the relevant traceability data generated
during the company operations [7]. Every single
data will distributed to the query application by
getting input product code from customer. The
retailer as one of end user will get traceability
reports after the process. This report answers the
following typical traceability questions for
instance:
- Result of tracing data product
- Generated recall list which contain all the
needed information to contact affected
customers and allow them to pull appropriate
products if indicated
- Report which identifies any of the suspect lots
or unit process with nonstandard procedure
II.
Fig 3. Business cycle of seeds data documenting
process
The stage of seeds handling process ended when
the seeds grow turning into larva that are ready to be
transferred into raising pond in the cultivation unit.
After the entire data was documented, data was
inputed from the seed data form into the desktop
computer. Data input application in desktop computer
would save all documented data, then printed the
report in the form of label with barcode from seeds ID
and pond destination for the process of raising the fish
larva. Data input application were illustrated in Fig 4.
COMPUTATIONAL EXPERIMENT
A computational experiment was set up to verify
and validate at what extend the proposed system could
fulfill the performance stakeholders required. A Java
based application system in both PC-Windows 7 and
Android-JellyBeans Machines was then constructed.
The details are as follows
356
ICACSIS 2014
handling. The analyzed data attribute were numerical
type showing a value. The results of factor analysis
showed attribute sequence that influence the system
and become the consideration in determining critical
data attribute that were needed to be constantly
documented. The attribute determination became a
source to create security system in source code input
data software so that the process of data input into
server was not available until data attribute filled in.
Table II shows example of relief method utilization to
determine critical attribute.
TABLE II
RESULTS OF FACTOR ANALYSIS WITH RELIEFF METHOD
TABLE I
DATA SEEDS DOCUMENTATION ON BREEDING FARM
No
Index
Date
0
12/08/2014
1
13/08/2014
2
14/08/2014
Batch
number
1
2
3
4
5
6
7
8
9
10
Broodstock
code
IU
IU
IU
IU
IU
IU
IU
IU
IU
IU
ID
2
6
10
8
4
9
7
1
5
3
Seeds
ID
31
32
33
34
35
36
37
38
39
40
Feed
Supplier ID
Code
No
P
003
P
001
P
001
P
003
P
003
P
001
P
003
P
001
P
003
P
003
Feed
ID
Supplier Ongrowing
Pond
Jordan
1A
Simon
2A
Simon
1B
Jordan
3A
Jordan
2B
Simon
2C
Jordan
3B
Simon
1C
Jordan
1D
Jordan
4A
Survive
Rates
(%)
97
97
96
95
97
97
97
96
97
97
Pond
pH of
Temperature Water
(°C)
23
7
23
7
23
7
24
8
25
7
24
7
23
9
24
9
25
7
24
9
Water
Salinity
(mg/l)
1212
1431
1308
1310
1301
1403
1100
1379
1159
1191
Note: This data follow the normal distribution random hypothetical.
Fig 4. Application of seed data input
Every process of data input was carried out at
the end of material handling process because
documenting data related to production process and
track record was carried out to the next stage during
the material handling process. Every stakeholder
carried out the same process namely data input
process into desktop computers to be stored in a
server. The printed barcode in label became the key
for product registration handled by using barcode
scanner (Fig 5). Application for other stakeholders
was deployed input documented data in the next
process of production.
Fig 5. Stage of data reading with barcode scanner
Data Attribute
Survive Rate
Average
Pond
Temperature
pH of water
Water Salinity
water
Relief Value
-0,4
-0,1
-0,3
-0,093
Rank
4
2
3
1
B. Tracing data
A database for this traceability information system
was constructed for data query using three
mathematic model follow as manual tracing process,
such as sorting, searching and check the suitable data
with similarity measurement, then arrange as source
code software by using java programming. This data
query application would be used by end user as one of
the Stakeholders roles for tracing distributed products
in customers. Figure of product tracing process at end
user stakeholder is shown on Fig 6.
In this explanation, the writer described the use of
the third mathematic model for the process of product
tracing by using data at the stage of seeds supply.
Computing stage from search mode in data query
application was initiated by location searching of date
data by utilizing interpolation search method with the
following equation:
Table I shows data in the stakeholder of seeds
supply unit documented during the ongoing system.
Data attributes were analyzed by using relief method
to figure out the most influenced factor and required
to be well documented during the process of product
357
Position =
BC Sought-BC [low]
BC [ high ] -BC [low]
× ( DI high-DI low ) + DI low (3)
Where BC is a broodstock code, DI is a data index,
BC [high] is the top level of BC and BC [low] is
bottom level of BC.
ICACSIS 2014
Data Index of location search from Table I data
uses equation model (3) was simulated in the
following calculation:
Search key
: 13
Data Index low : 0
Data Index high : 2
Position =
13–12
14–12
�(2 – 0) + 0 = 1
Data discovered in index [1] dated 13/08/2014 along
with other data in Tabel III.
Date
13/01/2014
13/01/2014
13/01/2014
13/01/2014
Broodstock ID
Code
no
IU
4
IU
9
IU
7
IU
1
seeds ID
Position =
7–1
9–1
� (3 – 0) + 0 = 2
Data discovered in index [2] seed ID 37 along with
other data attribute among others.
TABLE III
DATA BASED ON TABLE I
No
Index
1
The searched broodstock ID was IU7. However,
the code identification used in search process was
numerical data. Index number was the number of data
position in database arrangement. The search process
was explained as follows:
Search key
:7
Data Index low : 0
Data Index high : 3
…
35
36
37
38
Water Salinity
(mg/l)
1301
1403 Seeds
1100 ID
1379 37
Based on Table I
P003
Jordan
1100
(Based on Table 1)
The computing process by application enters the
next stage based on broodstock ID. Before the process
of data search was conducted, the broodstock ID was
processed by using insertion method with the
following stages:
- When i =1, x equals to Data [1] = 9 and j=0.
Because data [0] = 4 and 9 > 4 thus process was
continued for i=2.
- When i =2, x = Data [2] = 7 and j=1. Because
data [1] = 9 and 7 < 9, thus carried out shifting to
the left until data which was smaller than 7 was
discovered. Results of this shifting, Data [1] = 7
and Data [2] = 9 whilst Data [0] = x = 4.
- When i =3, x = Data [3] = 1 and =2. Because
Data [2] = 9 and 1 < 9, thus shifting was carried
out until data which was smaller than 1 was
discovered. Results of this shifting, Data [2] = 7
and Data [3] = 9 whilst Data [1] = x = 4. And so
forth
Fig 6. Stage of product tracing by end user
TABLE IV
DATA BASED ON TABLE I
Feed
Feed
… Water Salinity
Supplier ID
Supplier
(mg/l)
After the searched data were attained, hence
similarity measurement was carried out with standard
data attribute using Cosine Similarity [13] as follows:
xt . y
(4)
sim ( x, y ) =
,
|| x |||| y ||
Where x and y is two vectors for comparison, then ||x||
is the Euclidean norm of vector x = (x 1 , x 2 ,
x 3 ….x p ) which defined as:
(5)
x 2 + x 2 + ... + x 2
1
2
p
Standard data attribute in the form of numeric
value and factual data of search results is shown in
Table V.
TABLE V
STANDARD DATA ATTRIBUTE OF QUALITY REFERENCE
Attribute
standard
37
Symbol Survive Average Temp pH of
rate
(°C)
Water
X1
X2
98
97
25
23
8
9
Water
Salinity
(mg/l)
1300
1100
Suppose that X 1 and X 2 are the first two term frequency vectors in Table 4. That is, X 1 = (98, 25, 8,
1300) and X 2 = (97, 23, 9, 1100). How similar are X1
and X2? Using Eq. (4) and (5) to compute the cosine
similarity between the two vectors.
Based on the results of similarity measurement, it
indicated that the parameter of seeds quality from the
searched seeds ID was almost similar to the standard
parameter with value of 0,999. Thus, it could claim
that the seeds ID were in the standard of cultivation
process.
The dataset from ongrowing unit show in Table
VI. Pond ID was inter-correlated with data attribute
from ongrowing process, such as feed supplier ID and
other quality process for instance pH water,
temperature of water, survive rates, weight and
container temperature. During the harvesting days, the
data of yield and transportation are collected by an
application form to get data about harvest date and
harvest container ID which used to. The processing
358
ICACSIS 2014
stage followed the same documentation process to get
information about production line of shrimp
packaging and then transport to the cold storage. The
dataset of processing unit showed on Table VII.
In the same case, the data were available in order
to get further information about the product. The
identification of data utilizes by date other ID number
which get from supply chain event.
Traceability management information system
provided intangible advantages for instance
practicality, security and the deliverability of data for
each stakeholder. The data could save, organized and
emerged easier. Besides financial benefits, this system
given improvement in the quality of information for
management decision-making, and prevents in errors
documentation processes. The operator could save the
data on the data base system just by complete all field
on the data software application. Each data saved with
high security from lost and easy transferring to the
other stakeholder by the local network among the
computer. This system could provide information for
increasing
Fig 7. Report of tracing product
III.
CONCLUSIONS
TABEL VII
DATA
SHRIMP
TABEL
VI DOCUMENTATION FROM PROCESSING UNIT
DateDOCUMENTATION
Line
Pond FROM
Cold ONGROWING
Storage ID
Grade
Pack
DATA SHRIMP
UNIT(Box) @
Date
12102014
Pond
ID
2A
1A
Harvest
Container ID
HC2A01
HC2A02
HC2A03
HC1A01
HC1A02
HC1A03
packaging
ID
ID
Feed
Feed supplier
supplier ID
pH
water
12102014 PTL1
FS003
DevFeed2A
2C
1B
FS002
PTL2
Comfeed1A
3A
8
9
1C
1000 g
water
Survive
temperature A Rate B
(%)
(°C)
CS01 26
6
985
8
3
6
9
3
24
995
CS02
6
6
4
4
C
5
5
2
8
2
Cold
Temperature
Storage
Weight
Container
(°C)
Temperature
(g)
Temperature
(°C)
(°C)
5
2
16160
7
5
2
5
2
2
16780 5
7
5
2
6
5
2
Note: This data follow the normal distribution random hypothetical
customer confidence about the frozen shrimp product.
Some information included in the tracing report
such as:
1. Description of the product
2. Expired date of product
3. Flow chart of the process, include :
- Identification number of unit process or
material
- Temperature process and water pond
salinity
4. Similarity value of the process with standard
procedure and
5. Duration of tracing
Other report can be produced to give information
about recall list with contact number of affected
customers. The result of reporting can be seen on Fig
7.
Based on the analysis using BPMN 2.0, it is
known that there were five stakeholders taking role in
traceability system. The results of critical attribute
determination by using relief method determined that
water salinity parameter became critical attribute that
requires documentation. The results of mathematic
verification model show that the model used was able
to produce the expected parameter according to its
purpose. The rule of sorting method could show data
sorting process. Search method also proved that it
could be utilized for searching data location index.
Similarity measurement shows that data attributed
similar with standard process, thus the value almost 1
IV.
[1]
[2]
[3]
359
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ICACSIS 2014
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Evaluation on People Aspect in Knowledge Management System Implementation: A Case Study of Bank Indonesia
Putu Wuri Handayani
Page(s): 1-9
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Relative Density Estimation using Self-Organizing Maps
Denny
Page(s): 10-15
Abstract | Full Text: PDF
Multicore Computation of Tactical Integration System in the Maritime Patrol Aircraft using Intel Threading Building Block
Muhammad Faris Fathoni, Bambang Sridadi
Page(s): 16-21
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Government Knowledge Management System Analysis: Case Study Badan Kepegawaian Negara
Elin Cahyaningsih, lukman -, Dana Indra Sensuse
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Forecasting the Length of the Rainy Season Using Time Delay Neural Network
Agus Buono, Muhammad Asyhar Agmalaro, Amalia Fitranty Almira
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Hybrid Sampling for Multiclass Imbalanced Problem: Case Study of Students' Performance Prediction
Wanthanee Prachuabsupakij, Nuanwan Soonthornphisaj
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Interaction between users and buildings: results of a multicreteria analysis
Audrey Bona, Jean-Marc Salotti
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Digital watermarking in audio for copyright protection
Hemis Mustapha, Boudraa Bachir
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Multi-Grid Transformation for Medical Image Registration
Porawat Visutsak
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Creating Bahasa Indonesian - Javanese Parallel Corpora Using Wikipedia Articles
Bayu Distiawan Trisedya
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An Extension of Petri Network for Multi-Agent System Representation
Pierre Sauvage
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Gamified E-Learning Model Based on Community of Inquiry
Andika Yudha Utomo, Afifa Amriani, Alham Fikri Aji, Fatin Rohmah Nur Wahidah, Kasiyah M. Junus
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Model Prediction for Accreditation of Public Junior High School in Bogor Using Spatial Decision Tree
Endang Purnama Giri, Aniati Murni Arymurthy
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Application of Decision Tree Classifier for Single Nucleotide Polymorphism Discovery from Next-Generation Sequencing
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Muhammad Abrar Istiadi, Wisnu Ananta Kusuma, I Made Tasma
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Quality Evaluation of Airline’s E-Commerce Website, A Case Study of AirAsia and Lion Air Websites
Farah Shafira Effendi, Ika Alfina
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A comparative study of sound sources separation by Independent Component Analysis and Binaural Model
Bagus Tris Atmaja
Page(s): 94-98
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Enhancing Reliability of Feature Modeling with Transforming Representation into Abstract Behavioral Specification (ABS)
Muhammad Irfan Fadhillah
Page(s): 99-104
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Classification of Campus E-Complaint Documents using Directed Acyclic Graph Multi-Class SVM Based on Analytic
Hierarchy Process
Imam Cholissodin, Maya Kurniawati, Indriati, Issa Arwani
Page(s): 105-111
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Making Energy-saving Strategies: Using a Cue Offering Interface
Yasutaka Kishi, Kyoko Ito, Shogo Nishida
Page(s): 112-117
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Knowledge Management System Development with Evaluation Method in Lesson Study Activity
Murein Miksa Mardhia, Armein Z.R. Langi, Yoanes Bandung
Page(s): 118-123
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Extending V-model practices to support SRE to build Secure Web Application
Ala Ali Abdulrazeg
Page(s): 124-129
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Shared Service in E-Government Sector: Case Study of Implementation in Developed Countries
Ravika Hafizi, Suraya Miskon, Azizah Abdul Rahman
Page(s): 130-137
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Implementation of Steganography using LSB with Encrypted and Compressed Text using TEA-LZW on Android
Ledya Novamizanti
Page(s): 138-143
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Hotspot Clustering Using DBSCAN Algorithm and Shiny Web Framework
Karlina Khiyarin Nisa
Page(s): 144-147
Abstract | Full Text: PDF
Framework Model of Sustainable Supply Chain Risk for Dairy Agroindustry Based on Knowledge Base
Winnie Septiani
Page(s): 148-154
Abstract | Full Text: PDF
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94-98
16-21
57-63
256-261
318-323
410-414
47-51
Chanin Wongyai
Cliffen Allen
210-215
376-381
Dana Indra Sensuse
Darius Andana Haris
Darmawan Baginda Napitupulu
Dean Apriana Ramadhan
Denny
Devi Fitrianah
Diah E. Herwindiati
Dwi Hendratmo Widyantoro
Dyah E. Herwindiati
22-28, 289-294
376-381, 438-445
420-424
382-387
10-15
425-430
431-437
324-329
450-454
Elfira Febriani
Elin Cahyaningsih
Endang Purnama Giri
Enrico Budianto
Eri Prasetio Wibowo
Eric Punzalan
262-268
22-28
79-84, 216-221
492-497
467-471
155-160
Fadhilah Syafria
Fajar Munichputranto
Fajri Koto
336-341
262-268
193-197
B
C
D
E
F
Farah Shafira Effendi
Faris Al Afif
Fatin Rohmah Nur Wahidah
Febriana Misdianti
Firman Ardiansyah
90-93
484-491
72-78
330-335
204-209
Gladhi Guarddin
312-317
Hamidillah Ajie
Harish Muhammad Nazief
Harry Budi Santoso
Hemis Mustapha
Herman Tolle
Heru Sukoco
Husnul Khotimah
251-255
312-317
402-409
47-51
472-477
367-370
461-466
I Made Tasma
Ida Bagus Putu Peradnya Dinata
Ika Alfina
Ikhsanul Habibie
Ikhwana Elfitri
Imaduddin Amin
Imam Cholissodin
Imas Sukaesih Sitanggang
Indra Budi
Indriati
Irsyad Satria
Issa Arwani
Ito Wasito
Iwan Aang Soenandi
85-89
410-414
90-93
361-366, 492-497
307-311
324-329
105-111
166-170
256-261
105-111
342-347
105-111
446-449
283-288
Janson Hendryli
Jean-Marc Salotti
Jeanny Pragantha
Joel Ilao
John Derrick
Junaidy Budi Sanger
431-437
41-46
376-381
155-160
231-236
367-370
G
H
I
J
K
Karlina Khiyarin Nisa
Kasiyah M. Junus
Kyoko Ito
144-147
72-78
112-117
Lailan Sahrina Hasibuan
Ledya Novamizanti
222-226
138-143
M Anwar Ma'sum
M. Anwar Ma'sum
M. Iqbal Tawakal
Maria Ulfah Siregar
Maya Kurniawati
Meidy Layooari
Mira Suryani
Mohammad Uliniansyah
Muhammad Abrar Istiadi
Muhammad Asyhar Agmalaro
Muhammad Faris Fathoni
Muhammad Iqbal
Muhammad Irfan Fadhillah
Muhammad Octaviano Pratama
Muhammad Rifki Shihab
Muhammad Sakti Alvissalim
Murein Miksa Mardhia
394-401
484-491, 492-497
484-491
231-236
105-111
177-181
402-409
177-181
85-89
29-34
16-21
467-471
99-104
289-294
295-300, 301-306, 330-335
198-203
118-123
Ni Made Satvika Iswari
Nina Hairiyah
Nuanwan Soonthornphisaj
Nursidik Heru Praptono
171-176
262-268
35-40
425-430
Pauzi Ibrahim Nainggolan
Pierre Sauvage
Porawat Visutsak
Prane Mariel Ong
Prasetia Putra
Putu Satwika
161-165
64-71
52-56
155-160
251-255
492-497
L
M
N
P
Putu Wuri Handayani
1-9
Ralph Vincent Javellana Regalado
Ravika Hafizi
Reggio N Hartono
Riva Aktivia
Roger Luis Uy
246-250
130-137
177-181
455-460
155-160
Sani M. Isa
Satyanto Saptomo
Setia Damawan Afandi
Shogo Nishida
Sigit Prasetyo
Siobhan North
Sri Tiatri
Sri Wahyuni
Stanley Karouw
Stewart Sentanoe
Suraya Miskon
Syandra
431-437, 450-454
367-370
187-192
112-117
348-353
231-236
498-504
295-300
277-282
177-181
130-137
478-483
Taufik Djatna
Teny Handayani
Tji beng Jap
Tonny Adhi Sabastian
262-268, 283-288, 318-323, 354-360, 388-393, 455-460, 461-466
446-449
498-504
312-317
Vina Ayumi
289-294
Wanthanee Prachuabsupakij
Widodo Widodo
Wilson Fonda
Wina
Winnie Septiani
Wisnu Ananta Kusuma
Wisnu Jatmiko
35-40
251-255
371-375
450-454
148-154
85-89
484-491
R
S
T
V
W
Y
YB Dwi Setianto
Yani Nurhadryani
Yasutaka Kishi
Yaumil Miss Khoiriyah
Yoanes Bandung
Yudho Giri Sucahyo
Yustina Retno W. Utami
241-245
342-347, 455-460, 461-466
112-117
166-170
118-123
348-353
241-245
Zainal A. Hasibuan
lukman -
402-409
22-28
Z
Faculty of Computer Science - Universitas Indonesia ©2014
ICACSIS 2014
International Conference on
Advanced Computer Science and Information System 2012
(ICACSIS 2014)
Hotel Ambhara, Jakarta
October 18th - 19th, 2014
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Faculty of Computer Science - Universitas Indonesia ©2014
2014 International Conference on
Advanced Computer Science and Information Systems
(Proceedings)
Ambhara Hotel, Jakarta
October 18th-19th , 2014
Published by:
Faculty of Computer Science
Universitas Indonesia
ICACSIS 2014
An Analysis and Design of Frozen Shrimp
Traceability System Based on
Digital Business Ecosystem
Taufik Djatna and Aditia Ginantaka
Graduate Program of Agro-industrial Technology, Bogor Agricultural University, Indonesia
taufikdjatna@ipb.ac.id, aditiaginantaka@gmail.com
Abstract—Traceability system is one of the most
critical requirements in logistic information
systems and the supply chain risk management for
both global food safety and quality assurance.
Real-time documentation from the earlier stages of
production process enabled the two way process of
traceability. This paper presented an analysis and
design for traceability system of frozen Vanname
shrimp based on digital business ecosystems (DBE)
model. Business Process Model Notation (BPMN
2.0) was the primary tool in analyzing the task for
capturing and transferring data processing
between traceable units in each layer of DBE.
Business process analysis helped to understand the
capturing steps as the main element within such
traceability system. The results of the analysis
showcased how traceability system work in digital
business ecosystem which involved on dispersed
stakeholders. Manual data transformation to the
digital system was provided by stakeholders using
digital species metaphors. The requirement for
factor analysis was computed with Relief method
to select the most important attribute to capture.
Our evaluation showed that the proposed system
was able for estimating water salinity and related
hatchery parameters changing, such as broodstock
ID which utilized as key code. Current results
showed the readiness of application to transfer into
real world operation.
Keywords-traceability, digital business ecosystem,
traceable unit, digital species
I.
INTRODUCTION
Traceability is the ability to verify the history,
location, or application of an item by means of
documented recorded identification. Other common
definitions
include
the
capability
(and
implementation) of keeping track of a given set or
type of information to a given degree, or the ability to
chronologically interrelate uniquely identifiable
entities in a way that is verifiable [1].
Traceability system could use to solve the food
safety problems. The food safety context related to the
efficacy of comestibles that can become the cause of
diseases (bacteria, viruses, and germs) from one
country to others will thus harm people’s health when
consumed. This condition risked the potency of
rejection from importer countries. Several regulations
that control food safety give requirement to the
producers of comestibles to create a traceability
system. This system is used to trace and track the flow
of products on every supply chain mechanism in the
production and its distribution processes.
The traceability system offers some benefits
particularly in products with fresh and easily damaged
characteristics such as vegetables, fruit, meat, milk
and fish [2]. One of the efficacies of this system is
that it can improve security towards food safety from
the products it produced. This system enables
elimination towards unqualified food products from
the supply chain system as well as monitoring the
environment condition that can influence the safety of
products. Product certification gives significant
influence in costumer confidence. Thus, traceability
system is one of the requirements in ISO standard.
ISO 22005 is one of the documents that regulated the
design and implementation of traceability system in a
feed and food supply chain [3].
Shrimp is one of the leading exports of fishery
commodities. Indonesian export volume data show
that 122 tons shrimp has been exported in 2012 [4].
The shrimp company has to meet the criteria in ISO
standard which is regulated the traceability system to
get the confidence of importers, therefore the rejection
can be avoided. The traceability system is established
by documenting exceptional information on every
point of supply chain and process stages of product
handling, thus food producers are able to give detail
information on the food products. The information of
the product collected by supply chain actor, then
shared to another stakeholders. This process just
happen when the actor become the member of
business ecosystem in shrimp production. Therefore,
it is required to model the process of documenting
information on every point of supply chain which
catalyzed by ICTs infrastructure. Several researchers
have been conducted to find out the implementation
of the traceability system, such as in vegetables
supply chain [1], on soya beans [5] and in supply
chain of aquaculture products [7].
The utilization of digital technology has formed a
digital ecosystem in its business activity and therefore
called as Digital Business Ecosystem (DBE). The data
of documentation results can be stored in a database
hence query can be conducted for the search process.
The development of traceability system on DBE
base replaces all process documentation by using
paper (paper based), thus resulting paperless
document which benefits in the improvement in
354
ICACSIS 2014
overcoming easily damaged products by recording the
tracks of product quality, the improvement in product
management recall, the automatic scanning, the
improvement in stock management, and the decrease
in both work force operational cost. The pilots
traceability system based on DBE have been
implemented in a private sector company which
located in some sparsed Island. Their main business
was on hatchery and processed food. Frozen Vanname
shrimp chosen as the model because the complete
process of the supply chain, start from breeding unit
until the retailer or restaurant. This system’s
constructions were in terms of interest a company
which located in some sparse location of the
Indonesian archipelago. Each actor in the traceability
system is a model of organism in Digital Business
Ecosystem which interact each other in data capture
and tracing process. Through the development of
Digital Business Ecosystem, every technical phase in
the process of documenting data can be conducted by
using the support of ICT based infrastructure that is
analogized as digital species in digital ecosystem. The
technique of product search can also be represented in
the form of formulation and logic of computer
programming hence the digital application design is
attained for the process of data documenting and
searching (Fig.1).
Fig 1.Structural Coupling between supply chain
ecosystem and digital ecosystem in traceability
system [6]
The objective of this work were to analyze the
requirement and to design of traceability system. We
then focus to the proposed system for frozen
Vanname shrimp products and then verify and
validate the traceability system to evaluate system
performance.
I.
REQUIREMENT ANALYSIS
A. Business Process Analysis
Business process in traceability system is modeled
in BPMN 2.0. The Development of BPMN is
conducted. It is started from the making of simple
flow chart, granting information related roles, process,
data and information to description, therefore it can be
analyzed and simulated [8]. System analysis is
conducted for parse a system be resolved into
components so it the interaction between components
and its environment can be seen. Results of analysis
showed the capacity of the system as seen from its
ability to add value from input to output [9]. Based on
the business process analysis then retrieved five
stakeholders who take a role in the system of
traceability and divided into four structure systems.
1. Input
This system requires data related to product,
processes and product quality as main input [10].
Data related to product include its product identity
code along with various identity components that
support the formation of these products.
Meanwhile, the data related to the process cover
some of the indicators of the process that are set
up on the stage of the seed production. Among
them are pH of water, water salinity, survival rate,
and temperature of water. The data related to the
quality of the standard value according to SNI are
the total plate count (TPC), the levels of lead, the
levels of histamine and others. This system,
however, is not documenting related data quality
due to lack of infrastructure system.
2. Pre-process
The results of identification of the data attribute
are then observed and documented in the
application form for a period of time during
material handling process.
3. Process
The main processes include documentation of
process traceability system using an application
data input of each stakeholder and tracing product
process from end user stakeholder. Every
stakeholder
performs
the
process
of
documentation into application data input that was
installed in the desktop computer on each unit of
stakeholder. The process is then continued by
printing the report in the form of label contains
barcode of seed ID and destination pool for the
process of enlargement shrimp larvae. The
barcodes on the labels function as product
identification that can be read using barcode
scanner. Readable barcode labels subsequently can
be added to the data on the next process. The
barcode is printed back and imprinted on the next
product label [11].
Quantitative model in the process of
documentation of the data was then factorized
using Relief method. This analysis was performed
to find out the most influential data attribute in
traceable unit. There were some attribute data
defined as variables which were analyzed by using
Relief algorithm such as survive rates, average
pond water temperature, pH of water and water
salinity. That variable used as quality process
parameter in seed production, thus the variable
have to documented for complete information.
Different probability of the attribute X data value
calculated as follow [12]:
355
W [X ] =
pequal × Gini ' ( X )
psamecl (1 − psamecl )
(1)
ICACSIS 2014
Where,
A. Capturing Data
P (V )2
2
2
Gini ' ( X ) = ∑
× ∑ P ( C | V ) − ∑ P (C )
2
C
x∈ X ∑ P (V )
C
V
(2)
W [X] is an approximation of the following difference
of probabilities of attribute X, P equal is P(equal value
of X), P samecl is P(same class), P(V) is probability of
value, and P(C) is probable classes, P(C|V) is
probability X value occurred in a certain class C.
As a system, traceability must fulfill basic architecture
of Input-Process-Output components. The design of
data flow which transforms inputs into outputs is
represented in Fig. 2.
Every stakeholder carried out documenting
process into data input application installed on
desktop computer in each stakeholder unit. As an
example, in the unit of seeds provision, the
documented data attribute among others were
broodstock ID, seeds ID, provided feed mill supplier,
water temperature of the seeds pond, pH and water
salinity. The entire data attribute was documented
during the activity of seeds handling process in the
data application form on breeding unit. Figure of
business process stage of seeds documenting data can
be seen on Fig 3.
System Environment
Stakeholder:
- Breeding unit
- Ongrowing unit
- Processing unit
etc
Input:
Data Product
Data Process
Role:
- Capture data
- Tracing process
Output:
Tracing report
Traceability
System
Regulation
Recall Report
Digital
Device
Fig 2.The design of the data flow that transforms
inputs and outputs
4. Output
The data set will store in the traceability repository
include the relevant traceability data generated
during the company operations [7]. Every single
data will distributed to the query application by
getting input product code from customer. The
retailer as one of end user will get traceability
reports after the process. This report answers the
following typical traceability questions for
instance:
- Result of tracing data product
- Generated recall list which contain all the
needed information to contact affected
customers and allow them to pull appropriate
products if indicated
- Report which identifies any of the suspect lots
or unit process with nonstandard procedure
II.
Fig 3. Business cycle of seeds data documenting
process
The stage of seeds handling process ended when
the seeds grow turning into larva that are ready to be
transferred into raising pond in the cultivation unit.
After the entire data was documented, data was
inputed from the seed data form into the desktop
computer. Data input application in desktop computer
would save all documented data, then printed the
report in the form of label with barcode from seeds ID
and pond destination for the process of raising the fish
larva. Data input application were illustrated in Fig 4.
COMPUTATIONAL EXPERIMENT
A computational experiment was set up to verify
and validate at what extend the proposed system could
fulfill the performance stakeholders required. A Java
based application system in both PC-Windows 7 and
Android-JellyBeans Machines was then constructed.
The details are as follows
356
ICACSIS 2014
handling. The analyzed data attribute were numerical
type showing a value. The results of factor analysis
showed attribute sequence that influence the system
and become the consideration in determining critical
data attribute that were needed to be constantly
documented. The attribute determination became a
source to create security system in source code input
data software so that the process of data input into
server was not available until data attribute filled in.
Table II shows example of relief method utilization to
determine critical attribute.
TABLE II
RESULTS OF FACTOR ANALYSIS WITH RELIEFF METHOD
TABLE I
DATA SEEDS DOCUMENTATION ON BREEDING FARM
No
Index
Date
0
12/08/2014
1
13/08/2014
2
14/08/2014
Batch
number
1
2
3
4
5
6
7
8
9
10
Broodstock
code
IU
IU
IU
IU
IU
IU
IU
IU
IU
IU
ID
2
6
10
8
4
9
7
1
5
3
Seeds
ID
31
32
33
34
35
36
37
38
39
40
Feed
Supplier ID
Code
No
P
003
P
001
P
001
P
003
P
003
P
001
P
003
P
001
P
003
P
003
Feed
ID
Supplier Ongrowing
Pond
Jordan
1A
Simon
2A
Simon
1B
Jordan
3A
Jordan
2B
Simon
2C
Jordan
3B
Simon
1C
Jordan
1D
Jordan
4A
Survive
Rates
(%)
97
97
96
95
97
97
97
96
97
97
Pond
pH of
Temperature Water
(°C)
23
7
23
7
23
7
24
8
25
7
24
7
23
9
24
9
25
7
24
9
Water
Salinity
(mg/l)
1212
1431
1308
1310
1301
1403
1100
1379
1159
1191
Note: This data follow the normal distribution random hypothetical.
Fig 4. Application of seed data input
Every process of data input was carried out at
the end of material handling process because
documenting data related to production process and
track record was carried out to the next stage during
the material handling process. Every stakeholder
carried out the same process namely data input
process into desktop computers to be stored in a
server. The printed barcode in label became the key
for product registration handled by using barcode
scanner (Fig 5). Application for other stakeholders
was deployed input documented data in the next
process of production.
Fig 5. Stage of data reading with barcode scanner
Data Attribute
Survive Rate
Average
Pond
Temperature
pH of water
Water Salinity
water
Relief Value
-0,4
-0,1
-0,3
-0,093
Rank
4
2
3
1
B. Tracing data
A database for this traceability information system
was constructed for data query using three
mathematic model follow as manual tracing process,
such as sorting, searching and check the suitable data
with similarity measurement, then arrange as source
code software by using java programming. This data
query application would be used by end user as one of
the Stakeholders roles for tracing distributed products
in customers. Figure of product tracing process at end
user stakeholder is shown on Fig 6.
In this explanation, the writer described the use of
the third mathematic model for the process of product
tracing by using data at the stage of seeds supply.
Computing stage from search mode in data query
application was initiated by location searching of date
data by utilizing interpolation search method with the
following equation:
Table I shows data in the stakeholder of seeds
supply unit documented during the ongoing system.
Data attributes were analyzed by using relief method
to figure out the most influenced factor and required
to be well documented during the process of product
357
Position =
BC Sought-BC [low]
BC [ high ] -BC [low]
× ( DI high-DI low ) + DI low (3)
Where BC is a broodstock code, DI is a data index,
BC [high] is the top level of BC and BC [low] is
bottom level of BC.
ICACSIS 2014
Data Index of location search from Table I data
uses equation model (3) was simulated in the
following calculation:
Search key
: 13
Data Index low : 0
Data Index high : 2
Position =
13–12
14–12
�(2 – 0) + 0 = 1
Data discovered in index [1] dated 13/08/2014 along
with other data in Tabel III.
Date
13/01/2014
13/01/2014
13/01/2014
13/01/2014
Broodstock ID
Code
no
IU
4
IU
9
IU
7
IU
1
seeds ID
Position =
7–1
9–1
� (3 – 0) + 0 = 2
Data discovered in index [2] seed ID 37 along with
other data attribute among others.
TABLE III
DATA BASED ON TABLE I
No
Index
1
The searched broodstock ID was IU7. However,
the code identification used in search process was
numerical data. Index number was the number of data
position in database arrangement. The search process
was explained as follows:
Search key
:7
Data Index low : 0
Data Index high : 3
…
35
36
37
38
Water Salinity
(mg/l)
1301
1403 Seeds
1100 ID
1379 37
Based on Table I
P003
Jordan
1100
(Based on Table 1)
The computing process by application enters the
next stage based on broodstock ID. Before the process
of data search was conducted, the broodstock ID was
processed by using insertion method with the
following stages:
- When i =1, x equals to Data [1] = 9 and j=0.
Because data [0] = 4 and 9 > 4 thus process was
continued for i=2.
- When i =2, x = Data [2] = 7 and j=1. Because
data [1] = 9 and 7 < 9, thus carried out shifting to
the left until data which was smaller than 7 was
discovered. Results of this shifting, Data [1] = 7
and Data [2] = 9 whilst Data [0] = x = 4.
- When i =3, x = Data [3] = 1 and =2. Because
Data [2] = 9 and 1 < 9, thus shifting was carried
out until data which was smaller than 1 was
discovered. Results of this shifting, Data [2] = 7
and Data [3] = 9 whilst Data [1] = x = 4. And so
forth
Fig 6. Stage of product tracing by end user
TABLE IV
DATA BASED ON TABLE I
Feed
Feed
… Water Salinity
Supplier ID
Supplier
(mg/l)
After the searched data were attained, hence
similarity measurement was carried out with standard
data attribute using Cosine Similarity [13] as follows:
xt . y
(4)
sim ( x, y ) =
,
|| x |||| y ||
Where x and y is two vectors for comparison, then ||x||
is the Euclidean norm of vector x = (x 1 , x 2 ,
x 3 ….x p ) which defined as:
(5)
x 2 + x 2 + ... + x 2
1
2
p
Standard data attribute in the form of numeric
value and factual data of search results is shown in
Table V.
TABLE V
STANDARD DATA ATTRIBUTE OF QUALITY REFERENCE
Attribute
standard
37
Symbol Survive Average Temp pH of
rate
(°C)
Water
X1
X2
98
97
25
23
8
9
Water
Salinity
(mg/l)
1300
1100
Suppose that X 1 and X 2 are the first two term frequency vectors in Table 4. That is, X 1 = (98, 25, 8,
1300) and X 2 = (97, 23, 9, 1100). How similar are X1
and X2? Using Eq. (4) and (5) to compute the cosine
similarity between the two vectors.
Based on the results of similarity measurement, it
indicated that the parameter of seeds quality from the
searched seeds ID was almost similar to the standard
parameter with value of 0,999. Thus, it could claim
that the seeds ID were in the standard of cultivation
process.
The dataset from ongrowing unit show in Table
VI. Pond ID was inter-correlated with data attribute
from ongrowing process, such as feed supplier ID and
other quality process for instance pH water,
temperature of water, survive rates, weight and
container temperature. During the harvesting days, the
data of yield and transportation are collected by an
application form to get data about harvest date and
harvest container ID which used to. The processing
358
ICACSIS 2014
stage followed the same documentation process to get
information about production line of shrimp
packaging and then transport to the cold storage. The
dataset of processing unit showed on Table VII.
In the same case, the data were available in order
to get further information about the product. The
identification of data utilizes by date other ID number
which get from supply chain event.
Traceability management information system
provided intangible advantages for instance
practicality, security and the deliverability of data for
each stakeholder. The data could save, organized and
emerged easier. Besides financial benefits, this system
given improvement in the quality of information for
management decision-making, and prevents in errors
documentation processes. The operator could save the
data on the data base system just by complete all field
on the data software application. Each data saved with
high security from lost and easy transferring to the
other stakeholder by the local network among the
computer. This system could provide information for
increasing
Fig 7. Report of tracing product
III.
CONCLUSIONS
TABEL VII
DATA
SHRIMP
TABEL
VI DOCUMENTATION FROM PROCESSING UNIT
DateDOCUMENTATION
Line
Pond FROM
Cold ONGROWING
Storage ID
Grade
Pack
DATA SHRIMP
UNIT(Box) @
Date
12102014
Pond
ID
2A
1A
Harvest
Container ID
HC2A01
HC2A02
HC2A03
HC1A01
HC1A02
HC1A03
packaging
ID
ID
Feed
Feed supplier
supplier ID
pH
water
12102014 PTL1
FS003
DevFeed2A
2C
1B
FS002
PTL2
Comfeed1A
3A
8
9
1C
1000 g
water
Survive
temperature A Rate B
(%)
(°C)
CS01 26
6
985
8
3
6
9
3
24
995
CS02
6
6
4
4
C
5
5
2
8
2
Cold
Temperature
Storage
Weight
Container
(°C)
Temperature
(g)
Temperature
(°C)
(°C)
5
2
16160
7
5
2
5
2
2
16780 5
7
5
2
6
5
2
Note: This data follow the normal distribution random hypothetical
customer confidence about the frozen shrimp product.
Some information included in the tracing report
such as:
1. Description of the product
2. Expired date of product
3. Flow chart of the process, include :
- Identification number of unit process or
material
- Temperature process and water pond
salinity
4. Similarity value of the process with standard
procedure and
5. Duration of tracing
Other report can be produced to give information
about recall list with contact number of affected
customers. The result of reporting can be seen on Fig
7.
Based on the analysis using BPMN 2.0, it is
known that there were five stakeholders taking role in
traceability system. The results of critical attribute
determination by using relief method determined that
water salinity parameter became critical attribute that
requires documentation. The results of mathematic
verification model show that the model used was able
to produce the expected parameter according to its
purpose. The rule of sorting method could show data
sorting process. Search method also proved that it
could be utilized for searching data location index.
Similarity measurement shows that data attributed
similar with standard process, thus the value almost 1
IV.
[1]
[2]
[3]
359
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